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By Cristian La Rosa

Southeastern Archaeological Conference 2016

An archaeologist and GIS specialist from our Charleston office, Cristian La Rosa, will be presenting his recent research on phosphate mining in South Carolina at the 2016 annual Southeastern Archaeological Conference in Athens, Georgia. Mr. La Rosa's poster will be displayed on Friday, October 28th from 1-5 pm in the Athena E room. His title and abstract is included below, and the poster is attached as a PDF at the top of the article.

Using LiDAR to Identify and Analyze Landscape Features associated with Historic Phosphate Mines in Coastal South Carolina.

Phosphate Mining flourished from the mid-1860s to the late 1920s in areas adjacent to Charleston, South Carolina. The purpose of this poster is to demonstrate how Geographic Information Systems (GIS) and Light Detection and Ranging (LiDAR) technology can lead archaeologists to easily identify and analyze large-scale landscape features associated with historic phosphate mines. This is accomplished by creating a high-resolution digital elevation model (DEM), applying relief visualization techniques (r.skyview) and calculating terrain forms (r.geomorphon) using GIS geoprocessing tools.

New Interactive Hedekin Field Story Map

By Gitisha Goel and Clara Nguyen

Esri Southeast User Conference 2013

Gitisha Goel, a GIS and Graphics Specialist, and Clara Nguyen, our Designer and Web Specialist, recently completed a Story Map for Hedekin Field at Fort McPherson, an historic US Army military base in Atlanta, Georgia. Hedekin Field (Parade Field) is part of the oldest section of Fort McPherson that includes Staff Row and the Old Post Area. Hedekin Field includes 40 buildings that were constructed between 1891 and 1910, and have been listed on the National Register of Historic Places as an historic district. The buildings, arranged in orderly rows surrounding the Parade Field, include mess halls and quarters for officers and enlisted men. In 2012, as part of work done in conjunction with Base Realignment and Closure, completed an update of the National Register listing that expanded the historic district.

The Story Map created by Ms. Goel and Ms. Nguyen provides a new way for the public to view, learn about, and understand the historic military landscape at Hedekin Field.  Story Maps, published by GIS software company ESRI,  combine web maps with web applications and templates that can incorporate a range of information and media. The interactive map created for Hedekin Field allows users to view an historic map and modern aerial of the area, and by clicking on individual buildings, see historic and modern images of the building side-by-side. A brief history of each building is also provided, including the building's age, size, and purpose.

Easy to use, attractive, and interactive, Story Maps are great new outreach tool for culture resource management and historic preservation professionals to share information about historic places that may be inaccessible to members of the public.  They can also display time depth in a way that can be difficult to understand when viewing a static modern environment. Ms. Goel is looking forward to presenting the Hedekin Field Story Map at the ESRI Southeast User Conference from April 29-May 1, 2013, in Jacksonville, Florida.

To interact with the Hedekin Field Story Map, go to

By Thomas G. Whitley

2010 In, "Beyond the Artifact: Digital Interpretation of the Past", Franco Niccolucci and Sorin Hermon (editors), Archaeolingua, Budapest, Hungary, pp41-48.

Archaeological spatial analysis is a typically normative process. We tend to focus on the centralized locations of "things" such as sites or artifacts at the expense of identifying and evaluating "buffer zones" or boundaries. But how do we measure interactions between neighbors? Are there ways in which we can evaluate, understand and explain the creation and implementation of buffers, boundaries, territories, and trade routes? This paper will address means of extracting objective measures of "social distance" and relating them to the landscape in general. The perspective will be from an "immersive" point of view and one in which cognitive decision-making is emphasized. Several examples will be presented to illustrate the concepts.

By Thomas G. Whitley

2010 In, "Beyond the Artifact: Digital Interpretation of the Past", Franco Niccolucci and Sorin Hermon (editors), Archaeolingua, Budapest, Hungary, pp312-318.

Testing archaeological predictive models has almost always relied upon evaluating the percentage of sites "captured" versus the percentage of area defined as "high" potential. This is known as the "gain" statistic. Fundamentally inherent in correlative models and the gain statistic, though, is the assumption that measuring the deviation from randomness is the best method to evaluate the accuracy and precision of a model. This paper will show, in contrast, that the locations of archaeological sites are always dependent upon the location of the previous instance of settlement and therefore can act only like time-series dependent phenomena, never like random points. This calls for a fundamentally different means of testing models which can account for spatial autocorrelation.

By Thomas G. Whitley, Inna Moore, Gitisha Goel, and Damon Jackson

2010 In, "CAA 2009: Making History Interactive", Bernard Frischer, Jane Webb Crawford, and David Koller (editors), Archaeopress, Oxford, pp380-390

When we consider the intrinsic value of land units (or cells) in an archaeological analysis of landscape, settlement choice, or site selection, we tend to develop models which use static, unchanging costs or benefits, or which rely on least common denominators for a wide range of human actions or time frames. This is naturally driven by the tendency to find correlative evaluations as the most comforting means of both hypothesis building and hypothesis testing. Correlative approaches used in such applications as inductive predictive models are inherently reductionist and typically global-inferential. In actual application though, cell-based attractors are dynamic and distinctly contextual. Thus, we need to develop models that provide an egocentric, rather than a global, frame of reference, and are explanatory rather than merely correlative.

The first steps in this direction are provided by agent-based models; however, most agent-based models still utilize fixed frames of reference, or tools that rely on universal knowledge and global decision-making. Likewise, the acceptance of large dataset correlation testing, or training sets, as the primary means for assessing model success (even in agent-based models or neural network applications) precludes approaches that deal in sequential actions, local behaviors, or unique site types. Here we develop a model that uses cell-based analysis in several ways: First, attractor values are derivative of perception; the interface of knowledge and confidence in that knowledge. Second, spatial decision-making is temporally sequential; thus proximity tempers attractor values. And third, the scale of decision-making distinctly relies on both immediate and long-range planning and returns. These concepts will be illustrated with data from the Coastal Plain of Georgia (USA) and placed in the context of adaptations to a seemingly homogenous cultural and ecological landscape.

By Thomas G. Whitley, and Inna Moore

2008 In, "Digital Discovery: Exploring New Frontiers in Human Heritage", Jeffery T. Clark and Emily M. Hagemeister (editors), Archaeolingua, Budapest, Hungary, pp121-130

It has been argued at the CAA, and other conferences, in the last few years that archaeological predictive models which explain the relationships between the environment and human activity, rather than merely identifying presumed correlations, have the greatest potential to inform land management decision-making. Additionally, employing such models in a GIS framework allows us to examine some of the academic issues and preconceived ideas about human settlement that have been developed by the archaeologists working in a region. Recently, an explanatory approach to archaeological predictive modeling was designed and used for a large scale highway development project in eastern South Carolina. Encompassing four counties located almost entirely in the Coastal Plain, and covering more than 6500 square kilometers (~2600 square miles) this model was an ideal test for some of our notions about the nature of human settlement, procurement, and interactive behaviors. The results of this model suggest that an explanatory approach is more enlightening, more flexible, more efficient, more effective, and ultimately more useful than any other approach for this largely homogenous region. They also indicate that the approach could be employed anywhere, can be used to establish regional and/or local baselines on which to build with new information or ideas, and is adaptive to the needs of a particular project or study question.

By Thomas G. Whitley

2005 In, "Predictive Modelling for Archaeological Heritage Management: A Research Agenda", Martijn van Leusen and Hans Kamermans (editors), Nederlandse Archeologische Rapporten 29, Amersfoort: ROB, pp125-139.

In our effort to identify and manage the significant resources which comprise our cultural heritage, archaeologists have employed a number of methods which have focused on linking "sites" with key spatial factors in a predictive framework. Until recently these efforts have been largely correlative, deterministic, and devoid of social or interpretative theory. This has evolved into practical methods which lack an explication of causality, conflict with the intended economic or interpretative purposes of the undertaking, and relegate human cognition (both in the past and today) to being vaguely represented by a "black box" of uncertainty. In contrast, causality-based methods of cognitive modeling have the potential to produce ways of managing archaeological heritage, explaining patterns of cognition and behavior, and introducing agency and complexity into theories of human-spatial interaction. If the underlying causal relationships between conditions, events and decisions related to site selection are outlined in a mechanistic and probabilistic fashion, we may begin to understand why certain areas are selected for different kinds of behaviors, how that is transformed into what we consider to be "sites," and how we could use our knowledge to identify and protect significant archaeological resources. The methods presented here will be outlined on a theoretical basis, presented in a practical framework, and summarized as the intersection of three quite distinct kinds of models; past site selection, management priority, and disturbances.

By Thomas G. Whitley

2004 69th Annual Meeting of the Society for American Archaeology, Montreal, Canada

One of the benefits of GIS is the ability to rapidly analyze massive amounts of spatial data. This typically means using a "top-down" or aerial view of large expanses of terrain. Given that few inhabitants of any region make observations by orbiting satellite, we must assume that spatial choices are made using a series of cognitive landscapes visualized from an individual or "immersive" perspective. Through the creative use of standard spatial tools it is possible to simulate an immersive perspective for a wide variety of archaeological situations and address the sociocultural issues of decision-making, risk management, and site selection processes.

By Thomas G. Whitley

2003 In, "Enter the Past: The E-way into Four Dimensions of Cultural Heritage", Magistrat der Stadt Wien, Referat Kulturelles Erbe, Stadtarchaeologie Wien (editors), BAR International Series 1227, Archaeopress, U.K. pp236-239.

In recent years numerous archaeological approaches to predictive modeling have been presented in the literature. Most of these have taken the "inductive" perspective of applying known site locations to an analysis that estimates probable site location based on a mathematical equation and presents predictive surfaces in a GIS. Conversely, "deductive" models have also been used in which "expert systems" or site selection variables have been quantified as probability surfaces. There has been little discussion, though, of the differences between CRM and academic-based predictive modeling and how it has influenced the state of the "science" today. Generating more refined correlative predictive models either through the use of higher quality site location data or through more complex statistical techniques, runs counter to the implicit goals of CRM-based predictive modeling. A simple cognitive GIS approach which assumes a causal explanatory relationship creates comparable or better results (especially in homogenous areas) with no negative effects on these limited goals. Ultimately, the dichotomy between correlative and cognitive approaches is not in theoretical orientation, rather it is embodied in our understanding (or failure to understand) that correlative predictive modeling is really a tool useful only for land management, not interpretive archaeology.